Results for “ter”

Explains term sheet key terms, SAFE/convertible note structures, cap table dilution calculations, and negotiation red flags for startup fundraising.

Audit Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.

Benchmarks automatic post-editing (APE) models on WMT'18 SMT, SubEdits, and MLQE-PE datasets, reporting BLEU, ChrF, and TER scores computed with SacreBLEU and TERCOM.

Define and provision cloud infrastructure declaratively with Terraform, including reusable modules for Kubernetes clusters, databases, storage, and networking.

Create professional motion graphics and videos from the terminal.

Compresses responses to terse, fragment-style output while preserving code, errors, and technical terms, activating on brief-mode requests or trigger tokens.

Executes terminal commands, inspects git state, debugs CI or builds, and makes narrow fixes with exact proof of what was executed and verified.

Guide a visible, verified code walkthrough in Neovim with Terminal Control and Navi, optionally recording the presentation with OBS or retaining terminal evidence.

Infrastructure-as-code with Terraform/OpenTofu — resources, state, modules, plan/apply. Use when writing `.tf` files, managing cloud infrastructure with Terraform or OpenTofu, handling state and backends, or reviewing IaC code.

Terraform infrastructure-as-code workflow patterns: state and environments, module design, safe plan/apply, drift control, and CI guardrails

Comprehensive guide for working with HashiCorp Terraform Stacks. Use when creating, modifying, or validating Terraform Stack configurations (.tfcomponent.hcl, .tfdeploy.hcl files), working with stack components and deployments from local modules, public registry, or private registry sources, managing multi-region or multi-environment infrastructure, or troubleshooting Terraform Stacks syntax and structure.

Implement infrastructure as code with Terraform across AWS, Azure, or GCP, including module development, state management, provider configuration, multi-environment workflows, and infrastructure testing.

Write or review Terraform with clean versioned modules, locked remote state, drift detection, and disciplined plan review before apply.

Generates modular Terraform infrastructure-as-code for AWS, GCP, or Azure, including VPC, compute, database, cache, CDN, and monitoring with per-environment sizing, remote state, and cost estimates.

Guides developers through adding new RPC commands to Wave Terminal, covering interface definitions, type conventions, code generation, and implementation across server, Electron, frontend, remote, and terminal components.

Use when inspecting fields, indexed terms, or term counts from a local EDirect postings index rather than the remote `einfo` endpoint.

Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes.

Turn "who owns what" into fair, balanced territories reps will not fight over. Sets the segmentation, distributes accounts, checks balance across every rep, and writes the rules for disputes and inbound before they blow up in a QBR. Built for B2B sales and RevOps leaders, customizable to your segments and your CRM. Trigger on "design territories", "carve up the patches", "are these territories fair", "balance the book", "who gets inbound", or any territory or account-assignment build.

Terraform infrastructure-as-code agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Covers module design patterns, state management strategies, provider configuration, security hardening, policy-as-code with Sentinel/OPA, and CI/CD plan/apply workflows. Use when: user wants to design Terraform modules, manage state backends, review Terraform security, implement multi-region deployments, or follow IaC best practices.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Train, evaluate, export, and run inference on TAO action-recognition models for classifying temporal actions in video clips using RGB, optical flow, or joint input.

Trains, evaluates, exports, quantizes, and runs inference for SegFormer semantic segmentation models using NVIDIA TAO.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Trains, evaluates, exports, and runs inference on FoundationStereo models for stereo depth estimation and 3D reconstruction from stereo image pairs.

Trains, evaluates, exports, and runs inference for Siamese-network-based optical inspection models to detect manufacturing defects and quality issues in image pairs.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for OCDNet scene text detection models using TAO, detecting arbitrary-oriented text regions in natural images.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for TAO OCRNet models for scene text recognition from cropped text-region images, supporting CTC and attention-based decoders.

Train, evaluate, export, quantize, and run inference for a TAO OneFormer model that performs panoptic, instance, and semantic segmentation using task-conditioned queries.

Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.

Train, evaluate, export, and run inference for monocular depth estimation models using Metric Depth Anything v2 or Relative Depth Anything architectures via the TAO toolkit.